AI/ML Education at QMML
Hands-on, inclusive learning through workshops, tutorials, and practical ML sessions
Lesson 12: LSTMs and Stock Prediction Demo
A demo on how LSTMs are used in stock forecasting. Understand vanishing gradients and architecture tweaks.
Lesson 11: RNNs & LSTMs in PyTorch
Introduction to RNNs and LSTMs with a focus on NLP and time-series using PyTorch.
Lesson 10: Markov Decision Processes in RL
Understanding MDPs, Bellman equations, policy iteration, and exploration strategies in RL.
Lesson 9: RL to Beat the Market
Explore reinforcement learning through the K-armed bandit problem and its application to trading strategies.
Lesson 8: Implementing Large Language Models
Hands-on session covering LLMs, prompting, fine-tuning, deployment using OpenAI/Hugging Face APIs.
Lesson 7: Intro to Convolutional Neural Networks
Introduction to CNNs, including architecture (LeNet to ResNet), real-world applications, and training strategies.
Lesson 6: Classification
An overview of classification algorithms, evaluation metrics, and hands-on experience in building classifiers.
Lesson 5: Neural Networks II
Delves deeper into forward pass, loss functions, backpropagation, and an interactive workshop.
Lesson 4: Neural Networks
Introduction to neural networks, their architecture, activation functions, and a hands-on NumPy session.
Lesson 3: Multiple Linear Regression
Explores feature scaling, model evaluation with MSC and R², and an intro to overfitting/underfitting.
Lesson 2: Simple Linear Regression
Covers supervised learning, simple linear regression, and introduces the use of derivatives in practice.
Lesson 1: Introduction to Machine Learning
An introduction to Machine Learning, its real-world applications, Python basics, and fundamental mathematical concepts.